Technological advances in computer hardware, software and networking have lead to efficient, cost effective computing systems (e.g., desktop computers, laptops, handhelds, cell phones, servers, . . . ) that can communicate with each other from essentially anywhere in the world in order to exchange information. These systems continue to evolve into more reliable, robust and user-friendly systems. As a consequence, more and more industries and users are purchasing computers and utilizing them as viable electronic alternatives to traditional paper and verbal media for exchanging information. For example, many industries and users are leveraging computing technology to improve efficiency and decrease cost through web-based (e.g., on-line) services. For instance, users can search and retrieve particular information (e.g., via a search engine), view headlines related to available content, purchase goods, view bank statements, invoke monetary transactions (e.g., pay a bill on-line), research products and companies, apply for employment, obtain real-time stock quotes, obtain a college degree, download files and applications, transmit correspondence (e.g., email, chat rooms, . . . ), etc. with the click of a mouse.
As the availability of items (e.g., movies, music, photographs, e-mail, documents, text, word(s), phrase(s), files, video or sound clipets, messages, articles, web page(s), resources available on the World Wide Web, . . . ) utilized in connection with computing technology has increased, the task of effectively filtering, discovering, and managing these items has become increasingly more difficult and cumbersome. Conventional techniques have provided various personalization strategies to enable a user to more efficiently identify and/or access items of interest (e.g., via a search engine, headlines, . . . ). A typical personalization strategy utilizes an explicit input by the user indicating various interests, which can be employed to customize recommendations provided to the user. However, such a technique commonly requires the user to conduct initialization and can be subject to inaccuracies if the user fails to continually update the explicit input to match her current interest(s).
Another conventional technique that facilitates determining preferences of a user is collaborative filtering, which leverages a community to drive implicit personalization. A collaborative filtering system can yield predictions about interests of a user by collecting preference information from a number of users. However, most common collaborative filtering algorithms are not scalable, and thus are typically not able to be applied to large datasets such as datasets associated with the Internet, for example.